Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Distinct perceptual and conceptual representations of natural actions along the lateral and dorsal visual streams.

Communications biology·2026
Same author

Neural processing of naturalistic audiovisual events in space and time.

Communications biology·2025
Same author

Multivariate Pattern Analysis of EEG Reveals Neural Mechanism of Naturalistic Target Processing in Attentional Blink.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2024
Same author

Memoir study: Investigating image memorability across developmental stages.

PloS one·2023
Same author

Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder.

PLoS computational biology·2021
Same author

Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks.

Scientific reports·2020

相关实验视频

Updated: Jun 23, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.4K

有效的切片异常检测网络用于3D大脑MRI体积.

Zeduo Zhang1,2, Yalda Mohsenzadeh1,2

  • 1Department of Computer Science, Western University, London, Ontario, Canada.

PLOS digital health
|June 20, 2025
PubMed
概括
此摘要是机器生成的。

简单SliceNet通过使用2D图像功能来增强3D脑MRI扫描中的异常检测. 与现有的3D方法相比,这种新的方法提高了准确性和效率.

更多相关视频

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

7.4K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.9K

相关实验视频

Last Updated: Jun 23, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.4K
3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

7.4K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.9K

科学领域:

  • 医学成像分析分析 医学成像分析
  • 医疗保健中的人工智能
  • 神经科学是一个神经科学.

背景情况:

  • 异常检测方法因正常和异常的含糊不清的定义而与自然图像和医疗数据作斗争.
  • 目前的3D脑MRI异常检测依赖于记忆密集型,耗时的3D卷积神经网络,通常产生噪音的结果.

研究的目的:

  • 开发一个高效和准确的框架,用于在3D脑MRI卷中检测异常.
  • 克服现有的基于3D重建的模型的计算和准确性限制.

主要方法:

  • 建议使用预训练的2D特征提取器进行简单的基于切片的网络 (SimpleSliceNet).
  • 聚合2D切片功能用于3D体积异常检测.
  • 集成的条件正常化流量和对比损失,以提高精度.

主要成果:

  • 简单SliceNet在脑MRI异常检测方面表现出更好的性能和适应性.
  • 在精度,内存使用和大规模3D大脑体积的时间消耗方面超越了最先进的2D和3D模型.

结论:

  • 简单SliceNet为3D脑MRI异常检测提供了一个计算高效和高效的解决方案.
  • 该框架显示了临床应用的巨大潜力,需要精确识别脑部扫描中的偏差.